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<div class="header">
  <div class="summary">
<a href="#pub-methods">Public 成员函数</a> &#124;
<a href="#pro-static-methods">静态 Protected 成员函数</a> &#124;
<a href="#pri-attribs">Private 属性</a> &#124;
<a href="classpcl_1_1_fern_trainer-members.html">所有成员列表</a>  </div>
  <div class="headertitle">
<div class="title">pcl::FernTrainer&lt; FeatureType, DataSet, LabelType, ExampleIndex, NodeType &gt; 模板类 参考</div>  </div>
</div><!--header-->
<div class="contents">

<p>Trainer for a <a class="el" href="classpcl_1_1_fern.html" title="Class representing a Fern.">Fern</a>.  
 <a href="classpcl_1_1_fern_trainer.html#details">更多...</a></p>

<p><code>#include &lt;<a class="el" href="fern__trainer_8h_source.html">fern_trainer.h</a>&gt;</code></p>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public 成员函数</h2></td></tr>
<tr class="memitem:ac053613651cc00f38112296336bcecb1"><td class="memItemLeft" align="right" valign="top"><a id="ac053613651cc00f38112296336bcecb1"></a>
&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_fern_trainer.html#ac053613651cc00f38112296336bcecb1">FernTrainer</a> ()</td></tr>
<tr class="memdesc:ac053613651cc00f38112296336bcecb1"><td class="mdescLeft">&#160;</td><td class="mdescRight">Constructor. <br /></td></tr>
<tr class="separator:ac053613651cc00f38112296336bcecb1"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a7fe4e554a411f05d65380fbabbd2661f"><td class="memItemLeft" align="right" valign="top"><a id="a7fe4e554a411f05d65380fbabbd2661f"></a>
virtual&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_fern_trainer.html#a7fe4e554a411f05d65380fbabbd2661f">~FernTrainer</a> ()</td></tr>
<tr class="memdesc:a7fe4e554a411f05d65380fbabbd2661f"><td class="mdescLeft">&#160;</td><td class="mdescRight">Destructor. <br /></td></tr>
<tr class="separator:a7fe4e554a411f05d65380fbabbd2661f"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aa99e40796349298d52a27ba88025f72e"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_fern_trainer.html#aa99e40796349298d52a27ba88025f72e">setFeatureHandler</a> (<a class="el" href="classpcl_1_1_feature_handler.html">pcl::FeatureHandler</a>&lt; FeatureType, DataSet, ExampleIndex &gt; &amp;feature_handler)</td></tr>
<tr class="memdesc:aa99e40796349298d52a27ba88025f72e"><td class="mdescLeft">&#160;</td><td class="mdescRight">Sets the feature handler used to create and evaluate features.  <a href="classpcl_1_1_fern_trainer.html#aa99e40796349298d52a27ba88025f72e">更多...</a><br /></td></tr>
<tr class="separator:aa99e40796349298d52a27ba88025f72e"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a66d54e7c5815581f66703ac167657a4e"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_fern_trainer.html#a66d54e7c5815581f66703ac167657a4e">setStatsEstimator</a> (<a class="el" href="classpcl_1_1_stats_estimator.html">pcl::StatsEstimator</a>&lt; LabelType, NodeType, DataSet, ExampleIndex &gt; &amp;stats_estimator)</td></tr>
<tr class="memdesc:a66d54e7c5815581f66703ac167657a4e"><td class="mdescLeft">&#160;</td><td class="mdescRight">Sets the object for estimating the statistics for tree nodes.  <a href="classpcl_1_1_fern_trainer.html#a66d54e7c5815581f66703ac167657a4e">更多...</a><br /></td></tr>
<tr class="separator:a66d54e7c5815581f66703ac167657a4e"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a393464132c6f76e52f6c282c5e57c8ad"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_fern_trainer.html#a393464132c6f76e52f6c282c5e57c8ad">setFernDepth</a> (const size_t fern_depth)</td></tr>
<tr class="memdesc:a393464132c6f76e52f6c282c5e57c8ad"><td class="mdescLeft">&#160;</td><td class="mdescRight">Sets the maximum depth of the learned tree.  <a href="classpcl_1_1_fern_trainer.html#a393464132c6f76e52f6c282c5e57c8ad">更多...</a><br /></td></tr>
<tr class="separator:a393464132c6f76e52f6c282c5e57c8ad"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a193625899e3768cfa71ec8df945bfee8"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_fern_trainer.html#a193625899e3768cfa71ec8df945bfee8">setNumOfFeatures</a> (const size_t num_of_features)</td></tr>
<tr class="memdesc:a193625899e3768cfa71ec8df945bfee8"><td class="mdescLeft">&#160;</td><td class="mdescRight">Sets the number of features used to find optimal decision features.  <a href="classpcl_1_1_fern_trainer.html#a193625899e3768cfa71ec8df945bfee8">更多...</a><br /></td></tr>
<tr class="separator:a193625899e3768cfa71ec8df945bfee8"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a309fcebcc32b1e65fd71341477c97b39"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_fern_trainer.html#a309fcebcc32b1e65fd71341477c97b39">setNumOfThresholds</a> (const size_t num_of_threshold)</td></tr>
<tr class="memdesc:a309fcebcc32b1e65fd71341477c97b39"><td class="mdescLeft">&#160;</td><td class="mdescRight">Sets the number of thresholds tested for finding the optimal decision threshold on the feature responses.  <a href="classpcl_1_1_fern_trainer.html#a309fcebcc32b1e65fd71341477c97b39">更多...</a><br /></td></tr>
<tr class="separator:a309fcebcc32b1e65fd71341477c97b39"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a0fdc9754c4356310e7a05d9a52471a19"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_fern_trainer.html#a0fdc9754c4356310e7a05d9a52471a19">setTrainingDataSet</a> (DataSet &amp;data_set)</td></tr>
<tr class="memdesc:a0fdc9754c4356310e7a05d9a52471a19"><td class="mdescLeft">&#160;</td><td class="mdescRight">Sets the input data set used for training.  <a href="classpcl_1_1_fern_trainer.html#a0fdc9754c4356310e7a05d9a52471a19">更多...</a><br /></td></tr>
<tr class="separator:a0fdc9754c4356310e7a05d9a52471a19"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:acc94b1fd0ff0c5c02e61e012eb7de632"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_fern_trainer.html#acc94b1fd0ff0c5c02e61e012eb7de632">setExamples</a> (std::vector&lt; ExampleIndex &gt; &amp;examples)</td></tr>
<tr class="memdesc:acc94b1fd0ff0c5c02e61e012eb7de632"><td class="mdescLeft">&#160;</td><td class="mdescRight">Example indices that specify the data used for training.  <a href="classpcl_1_1_fern_trainer.html#acc94b1fd0ff0c5c02e61e012eb7de632">更多...</a><br /></td></tr>
<tr class="separator:acc94b1fd0ff0c5c02e61e012eb7de632"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ac53828369b23a3868e8e7c4441bb9555"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_fern_trainer.html#ac53828369b23a3868e8e7c4441bb9555">setLabelData</a> (std::vector&lt; LabelType &gt; &amp;label_data)</td></tr>
<tr class="memdesc:ac53828369b23a3868e8e7c4441bb9555"><td class="mdescLeft">&#160;</td><td class="mdescRight">Sets the label data corresponding to the example data.  <a href="classpcl_1_1_fern_trainer.html#ac53828369b23a3868e8e7c4441bb9555">更多...</a><br /></td></tr>
<tr class="separator:ac53828369b23a3868e8e7c4441bb9555"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a954e9758098d4f7718b649f2866a0ba6"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_fern_trainer.html#a954e9758098d4f7718b649f2866a0ba6">train</a> (<a class="el" href="classpcl_1_1_fern.html">Fern</a>&lt; FeatureType, NodeType &gt; &amp;fern)</td></tr>
<tr class="memdesc:a954e9758098d4f7718b649f2866a0ba6"><td class="mdescLeft">&#160;</td><td class="mdescRight">Trains a decision tree using the set training data and settings.  <a href="classpcl_1_1_fern_trainer.html#a954e9758098d4f7718b649f2866a0ba6">更多...</a><br /></td></tr>
<tr class="separator:a954e9758098d4f7718b649f2866a0ba6"><td class="memSeparator" colspan="2">&#160;</td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pro-static-methods"></a>
静态 Protected 成员函数</h2></td></tr>
<tr class="memitem:a512ae37a9a02fed93fb47841ef3b0fad"><td class="memItemLeft" align="right" valign="top">static void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_fern_trainer.html#a512ae37a9a02fed93fb47841ef3b0fad">createThresholdsUniform</a> (const size_t num_of_thresholds, std::vector&lt; float &gt; &amp;values, std::vector&lt; float &gt; &amp;thresholds)</td></tr>
<tr class="memdesc:a512ae37a9a02fed93fb47841ef3b0fad"><td class="mdescLeft">&#160;</td><td class="mdescRight">Creates uniformely distrebuted thresholds over the range of the supplied values.  <a href="classpcl_1_1_fern_trainer.html#a512ae37a9a02fed93fb47841ef3b0fad">更多...</a><br /></td></tr>
<tr class="separator:a512ae37a9a02fed93fb47841ef3b0fad"><td class="memSeparator" colspan="2">&#160;</td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pri-attribs"></a>
Private 属性</h2></td></tr>
<tr class="memitem:a03a692d065ffc34771ce493ccb0d04af"><td class="memItemLeft" align="right" valign="top"><a id="a03a692d065ffc34771ce493ccb0d04af"></a>
size_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_fern_trainer.html#a03a692d065ffc34771ce493ccb0d04af">fern_depth_</a></td></tr>
<tr class="memdesc:a03a692d065ffc34771ce493ccb0d04af"><td class="mdescLeft">&#160;</td><td class="mdescRight">Desired depth of the learned fern. <br /></td></tr>
<tr class="separator:a03a692d065ffc34771ce493ccb0d04af"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aebcda0ed2e8099602c7852abb5787296"><td class="memItemLeft" align="right" valign="top"><a id="aebcda0ed2e8099602c7852abb5787296"></a>
size_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_fern_trainer.html#aebcda0ed2e8099602c7852abb5787296">num_of_features_</a></td></tr>
<tr class="memdesc:aebcda0ed2e8099602c7852abb5787296"><td class="mdescLeft">&#160;</td><td class="mdescRight">Number of features used to find optimal decision features. <br /></td></tr>
<tr class="separator:aebcda0ed2e8099602c7852abb5787296"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ac367be1da0dc3cad1c8540b41b20cf20"><td class="memItemLeft" align="right" valign="top"><a id="ac367be1da0dc3cad1c8540b41b20cf20"></a>
size_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_fern_trainer.html#ac367be1da0dc3cad1c8540b41b20cf20">num_of_thresholds_</a></td></tr>
<tr class="memdesc:ac367be1da0dc3cad1c8540b41b20cf20"><td class="mdescLeft">&#160;</td><td class="mdescRight">Number of thresholds. <br /></td></tr>
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<tr class="memitem:ae2d6f63d91d81db9501befb224a4eded"><td class="memItemLeft" align="right" valign="top"><a id="ae2d6f63d91d81db9501befb224a4eded"></a>
<a class="el" href="classpcl_1_1_feature_handler.html">pcl::FeatureHandler</a>&lt; FeatureType, DataSet, ExampleIndex &gt; *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_fern_trainer.html#ae2d6f63d91d81db9501befb224a4eded">feature_handler_</a></td></tr>
<tr class="memdesc:ae2d6f63d91d81db9501befb224a4eded"><td class="mdescLeft">&#160;</td><td class="mdescRight"><a class="el" href="classpcl_1_1_feature_handler.html" title="Utility class interface which is used for creating and evaluating features.">FeatureHandler</a> instance, responsible for creating and evaluating features. <br /></td></tr>
<tr class="separator:ae2d6f63d91d81db9501befb224a4eded"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aff950066a956e60191a56ba25a976010"><td class="memItemLeft" align="right" valign="top"><a id="aff950066a956e60191a56ba25a976010"></a>
<a class="el" href="classpcl_1_1_stats_estimator.html">pcl::StatsEstimator</a>&lt; LabelType, NodeType, DataSet, ExampleIndex &gt; *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_fern_trainer.html#aff950066a956e60191a56ba25a976010">stats_estimator_</a></td></tr>
<tr class="memdesc:aff950066a956e60191a56ba25a976010"><td class="mdescLeft">&#160;</td><td class="mdescRight"><a class="el" href="classpcl_1_1_stats_estimator.html" title="Class interface for gathering statistics for decision tree learning.">StatsEstimator</a> instance, responsible for gathering stats about a node. <br /></td></tr>
<tr class="separator:aff950066a956e60191a56ba25a976010"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a87f77780925ef928934e71da1ca67d8c"><td class="memItemLeft" align="right" valign="top"><a id="a87f77780925ef928934e71da1ca67d8c"></a>
DataSet&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_fern_trainer.html#a87f77780925ef928934e71da1ca67d8c">data_set_</a></td></tr>
<tr class="memdesc:a87f77780925ef928934e71da1ca67d8c"><td class="mdescLeft">&#160;</td><td class="mdescRight">The training data set. <br /></td></tr>
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<tr class="memitem:a90bbfae4e2ceb1a5469c6933b976964c"><td class="memItemLeft" align="right" valign="top"><a id="a90bbfae4e2ceb1a5469c6933b976964c"></a>
std::vector&lt; LabelType &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_fern_trainer.html#a90bbfae4e2ceb1a5469c6933b976964c">label_data_</a></td></tr>
<tr class="memdesc:a90bbfae4e2ceb1a5469c6933b976964c"><td class="mdescLeft">&#160;</td><td class="mdescRight">The label data. <br /></td></tr>
<tr class="separator:a90bbfae4e2ceb1a5469c6933b976964c"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ae1e4719f1772e3b9f7e1fe6e6440c7c0"><td class="memItemLeft" align="right" valign="top"><a id="ae1e4719f1772e3b9f7e1fe6e6440c7c0"></a>
std::vector&lt; ExampleIndex &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_fern_trainer.html#ae1e4719f1772e3b9f7e1fe6e6440c7c0">examples_</a></td></tr>
<tr class="memdesc:ae1e4719f1772e3b9f7e1fe6e6440c7c0"><td class="mdescLeft">&#160;</td><td class="mdescRight">The example data. <br /></td></tr>
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<a name="details" id="details"></a><h2 class="groupheader">详细描述</h2>
<div class="textblock"><h3>template&lt;class FeatureType, class DataSet, class LabelType, class ExampleIndex, class NodeType&gt;<br />
class pcl::FernTrainer&lt; FeatureType, DataSet, LabelType, ExampleIndex, NodeType &gt;</h3>

<p>Trainer for a <a class="el" href="classpcl_1_1_fern.html" title="Class representing a Fern.">Fern</a>. </p>
</div><h2 class="groupheader">成员函数说明</h2>
<a id="a512ae37a9a02fed93fb47841ef3b0fad"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a512ae37a9a02fed93fb47841ef3b0fad">&#9670;&nbsp;</a></span>createThresholdsUniform()</h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType &gt; </div>
<table class="mlabels">
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          <td class="memname">void <a class="el" href="classpcl_1_1_fern_trainer.html">pcl::FernTrainer</a>&lt; FeatureType, DataSet, LabelType, ExampleIndex, NodeType &gt;::createThresholdsUniform </td>
          <td>(</td>
          <td class="paramtype">const size_t&#160;</td>
          <td class="paramname"><em>num_of_thresholds</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; float &gt; &amp;&#160;</td>
          <td class="paramname"><em>values</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; float &gt; &amp;&#160;</td>
          <td class="paramname"><em>thresholds</em>&#160;</td>
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<p>Creates uniformely distrebuted thresholds over the range of the supplied values. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">num_of_thresholds</td><td>The number of thresholds to create. </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">values</td><td>The values for estimating the expected value range. </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">thresholds</td><td>The resulting thresholds. </td></tr>
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<div class="fragment"><div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;{</div>
<div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;  <span class="comment">// estimate range of values</span></div>
<div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;  <span class="keywordtype">float</span> min_value = ::std::numeric_limits&lt;float&gt;::max();</div>
<div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;  <span class="keywordtype">float</span> max_value = -::std::numeric_limits&lt;float&gt;::max();</div>
<div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160; </div>
<div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160;  <span class="keyword">const</span> <span class="keywordtype">size_t</span> num_of_values = values.size ();</div>
<div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> value_index = 0; value_index &lt; num_of_values; ++value_index)</div>
<div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160;  {</div>
<div class="line"><a name="l00271"></a><span class="lineno">  271</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">float</span> value = values[value_index];</div>
<div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160; </div>
<div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;    <span class="keywordflow">if</span> (value &lt; min_value) min_value = value;</div>
<div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160;    <span class="keywordflow">if</span> (value &gt; max_value) max_value = value;</div>
<div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;  }</div>
<div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160; </div>
<div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;  <span class="keyword">const</span> <span class="keywordtype">float</span> range = max_value - min_value;</div>
<div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;  <span class="keyword">const</span> <span class="keywordtype">float</span> step = range / (num_of_thresholds+2);</div>
<div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160; </div>
<div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;  <span class="comment">// compute thresholds</span></div>
<div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;  thresholds.resize (num_of_thresholds);</div>
<div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160; </div>
<div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> threshold_index = 0; threshold_index &lt; num_of_thresholds; ++threshold_index)</div>
<div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160;  {</div>
<div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;    thresholds[threshold_index] = min_value + step*(threshold_index+1);</div>
<div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160;  }</div>
<div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160;}</div>
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<h2 class="memtitle"><span class="permalink"><a href="#acc94b1fd0ff0c5c02e61e012eb7de632">&#9670;&nbsp;</a></span>setExamples()</h2>

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template&lt;class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType &gt; </div>
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          <td class="memname">void <a class="el" href="classpcl_1_1_fern_trainer.html">pcl::FernTrainer</a>&lt; FeatureType, DataSet, LabelType, ExampleIndex, NodeType &gt;::setExamples </td>
          <td>(</td>
          <td class="paramtype">std::vector&lt; ExampleIndex &gt; &amp;&#160;</td>
          <td class="paramname"><em>examples</em></td><td>)</td>
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<p>Example indices that specify the data used for training. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">examples</td><td>The examples. </td></tr>
  </table>
  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;      {</div>
<div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;        <a class="code" href="classpcl_1_1_fern_trainer.html#ae1e4719f1772e3b9f7e1fe6e6440c7c0">examples_</a> = examples;</div>
<div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;      }</div>
<div class="ttc" id="aclasspcl_1_1_fern_trainer_html_ae1e4719f1772e3b9f7e1fe6e6440c7c0"><div class="ttname"><a href="classpcl_1_1_fern_trainer.html#ae1e4719f1772e3b9f7e1fe6e6440c7c0">pcl::FernTrainer::examples_</a></div><div class="ttdeci">std::vector&lt; ExampleIndex &gt; examples_</div><div class="ttdoc">The example data.</div><div class="ttdef"><b>Definition:</b> fern_trainer.h:174</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#aa99e40796349298d52a27ba88025f72e">&#9670;&nbsp;</a></span>setFeatureHandler()</h2>

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template&lt;class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType &gt; </div>
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          <td class="memname">void <a class="el" href="classpcl_1_1_fern_trainer.html">pcl::FernTrainer</a>&lt; FeatureType, DataSet, LabelType, ExampleIndex, NodeType &gt;::setFeatureHandler </td>
          <td>(</td>
          <td class="paramtype"><a class="el" href="classpcl_1_1_feature_handler.html">pcl::FeatureHandler</a>&lt; FeatureType, DataSet, ExampleIndex &gt; &amp;&#160;</td>
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<p>Sets the feature handler used to create and evaluate features. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">feature_handler</td><td>The feature handler. </td></tr>
  </table>
  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;      {</div>
<div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;        <a class="code" href="classpcl_1_1_fern_trainer.html#ae2d6f63d91d81db9501befb224a4eded">feature_handler_</a> = &amp;feature_handler;</div>
<div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;      }</div>
<div class="ttc" id="aclasspcl_1_1_fern_trainer_html_ae2d6f63d91d81db9501befb224a4eded"><div class="ttname"><a href="classpcl_1_1_fern_trainer.html#ae2d6f63d91d81db9501befb224a4eded">pcl::FernTrainer::feature_handler_</a></div><div class="ttdeci">pcl::FeatureHandler&lt; FeatureType, DataSet, ExampleIndex &gt; * feature_handler_</div><div class="ttdoc">FeatureHandler instance, responsible for creating and evaluating features.</div><div class="ttdef"><b>Definition:</b> fern_trainer.h:165</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#a393464132c6f76e52f6c282c5e57c8ad">&#9670;&nbsp;</a></span>setFernDepth()</h2>

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template&lt;class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType &gt; </div>
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          <td class="memname">void <a class="el" href="classpcl_1_1_fern_trainer.html">pcl::FernTrainer</a>&lt; FeatureType, DataSet, LabelType, ExampleIndex, NodeType &gt;::setFernDepth </td>
          <td>(</td>
          <td class="paramtype">const size_t&#160;</td>
          <td class="paramname"><em>fern_depth</em></td><td>)</td>
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<p>Sets the maximum depth of the learned tree. </p>
<dl class="params"><dt>参数</dt><dd>
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    <tr><td class="paramdir">[in]</td><td class="paramname">fern_depth</td><td>Maximum depth of the learned tree. </td></tr>
  </table>
  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;      {</div>
<div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;        <a class="code" href="classpcl_1_1_fern_trainer.html#a03a692d065ffc34771ce493ccb0d04af">fern_depth_</a> = fern_depth;</div>
<div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;      }</div>
<div class="ttc" id="aclasspcl_1_1_fern_trainer_html_a03a692d065ffc34771ce493ccb0d04af"><div class="ttname"><a href="classpcl_1_1_fern_trainer.html#a03a692d065ffc34771ce493ccb0d04af">pcl::FernTrainer::fern_depth_</a></div><div class="ttdeci">size_t fern_depth_</div><div class="ttdoc">Desired depth of the learned fern.</div><div class="ttdef"><b>Definition:</b> fern_trainer.h:158</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#ac53828369b23a3868e8e7c4441bb9555">&#9670;&nbsp;</a></span>setLabelData()</h2>

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template&lt;class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType &gt; </div>
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          <td class="memname">void <a class="el" href="classpcl_1_1_fern_trainer.html">pcl::FernTrainer</a>&lt; FeatureType, DataSet, LabelType, ExampleIndex, NodeType &gt;::setLabelData </td>
          <td>(</td>
          <td class="paramtype">std::vector&lt; LabelType &gt; &amp;&#160;</td>
          <td class="paramname"><em>label_data</em></td><td>)</td>
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<p>Sets the label data corresponding to the example data. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">label_data</td><td>The label data. </td></tr>
  </table>
  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;      {</div>
<div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;        <a class="code" href="classpcl_1_1_fern_trainer.html#a90bbfae4e2ceb1a5469c6933b976964c">label_data_</a> = label_data;</div>
<div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;      }</div>
<div class="ttc" id="aclasspcl_1_1_fern_trainer_html_a90bbfae4e2ceb1a5469c6933b976964c"><div class="ttname"><a href="classpcl_1_1_fern_trainer.html#a90bbfae4e2ceb1a5469c6933b976964c">pcl::FernTrainer::label_data_</a></div><div class="ttdeci">std::vector&lt; LabelType &gt; label_data_</div><div class="ttdoc">The label data.</div><div class="ttdef"><b>Definition:</b> fern_trainer.h:172</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#a193625899e3768cfa71ec8df945bfee8">&#9670;&nbsp;</a></span>setNumOfFeatures()</h2>

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template&lt;class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType &gt; </div>
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          <td class="memname">void <a class="el" href="classpcl_1_1_fern_trainer.html">pcl::FernTrainer</a>&lt; FeatureType, DataSet, LabelType, ExampleIndex, NodeType &gt;::setNumOfFeatures </td>
          <td>(</td>
          <td class="paramtype">const size_t&#160;</td>
          <td class="paramname"><em>num_of_features</em></td><td>)</td>
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<p>Sets the number of features used to find optimal decision features. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">num_of_features</td><td>The number of features. </td></tr>
  </table>
  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;      {</div>
<div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;        <a class="code" href="classpcl_1_1_fern_trainer.html#aebcda0ed2e8099602c7852abb5787296">num_of_features_</a> = num_of_features;</div>
<div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;      }</div>
<div class="ttc" id="aclasspcl_1_1_fern_trainer_html_aebcda0ed2e8099602c7852abb5787296"><div class="ttname"><a href="classpcl_1_1_fern_trainer.html#aebcda0ed2e8099602c7852abb5787296">pcl::FernTrainer::num_of_features_</a></div><div class="ttdeci">size_t num_of_features_</div><div class="ttdoc">Number of features used to find optimal decision features.</div><div class="ttdef"><b>Definition:</b> fern_trainer.h:160</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#a309fcebcc32b1e65fd71341477c97b39">&#9670;&nbsp;</a></span>setNumOfThresholds()</h2>

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template&lt;class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType &gt; </div>
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          <td class="memname">void <a class="el" href="classpcl_1_1_fern_trainer.html">pcl::FernTrainer</a>&lt; FeatureType, DataSet, LabelType, ExampleIndex, NodeType &gt;::setNumOfThresholds </td>
          <td>(</td>
          <td class="paramtype">const size_t&#160;</td>
          <td class="paramname"><em>num_of_threshold</em></td><td>)</td>
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<p>Sets the number of thresholds tested for finding the optimal decision threshold on the feature responses. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">num_of_threshold</td><td>The number of thresholds. </td></tr>
  </table>
  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;      {</div>
<div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;        <a class="code" href="classpcl_1_1_fern_trainer.html#ac367be1da0dc3cad1c8540b41b20cf20">num_of_thresholds_</a> = num_of_threshold;</div>
<div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;      }</div>
<div class="ttc" id="aclasspcl_1_1_fern_trainer_html_ac367be1da0dc3cad1c8540b41b20cf20"><div class="ttname"><a href="classpcl_1_1_fern_trainer.html#ac367be1da0dc3cad1c8540b41b20cf20">pcl::FernTrainer::num_of_thresholds_</a></div><div class="ttdeci">size_t num_of_thresholds_</div><div class="ttdoc">Number of thresholds.</div><div class="ttdef"><b>Definition:</b> fern_trainer.h:162</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#a66d54e7c5815581f66703ac167657a4e">&#9670;&nbsp;</a></span>setStatsEstimator()</h2>

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template&lt;class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType &gt; </div>
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          <td class="memname">void <a class="el" href="classpcl_1_1_fern_trainer.html">pcl::FernTrainer</a>&lt; FeatureType, DataSet, LabelType, ExampleIndex, NodeType &gt;::setStatsEstimator </td>
          <td>(</td>
          <td class="paramtype"><a class="el" href="classpcl_1_1_stats_estimator.html">pcl::StatsEstimator</a>&lt; LabelType, NodeType, DataSet, ExampleIndex &gt; &amp;&#160;</td>
          <td class="paramname"><em>stats_estimator</em></td><td>)</td>
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<p>Sets the object for estimating the statistics for tree nodes. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">stats_estimator</td><td>The statistics estimator. </td></tr>
  </table>
  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;      {</div>
<div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;        <a class="code" href="classpcl_1_1_fern_trainer.html#aff950066a956e60191a56ba25a976010">stats_estimator_</a> = &amp;stats_estimator;</div>
<div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;      }</div>
<div class="ttc" id="aclasspcl_1_1_fern_trainer_html_aff950066a956e60191a56ba25a976010"><div class="ttname"><a href="classpcl_1_1_fern_trainer.html#aff950066a956e60191a56ba25a976010">pcl::FernTrainer::stats_estimator_</a></div><div class="ttdeci">pcl::StatsEstimator&lt; LabelType, NodeType, DataSet, ExampleIndex &gt; * stats_estimator_</div><div class="ttdoc">StatsEstimator instance, responsible for gathering stats about a node.</div><div class="ttdef"><b>Definition:</b> fern_trainer.h:167</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#a0fdc9754c4356310e7a05d9a52471a19">&#9670;&nbsp;</a></span>setTrainingDataSet()</h2>

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template&lt;class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType &gt; </div>
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          <td class="memname">void <a class="el" href="classpcl_1_1_fern_trainer.html">pcl::FernTrainer</a>&lt; FeatureType, DataSet, LabelType, ExampleIndex, NodeType &gt;::setTrainingDataSet </td>
          <td>(</td>
          <td class="paramtype">DataSet &amp;&#160;</td>
          <td class="paramname"><em>data_set</em></td><td>)</td>
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<p>Sets the input data set used for training. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">data_set</td><td>The data set used for training. </td></tr>
  </table>
  </dd>
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<div class="fragment"><div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;      {</div>
<div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;        <a class="code" href="classpcl_1_1_fern_trainer.html#a87f77780925ef928934e71da1ca67d8c">data_set_</a> = data_set;</div>
<div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;      }</div>
<div class="ttc" id="aclasspcl_1_1_fern_trainer_html_a87f77780925ef928934e71da1ca67d8c"><div class="ttname"><a href="classpcl_1_1_fern_trainer.html#a87f77780925ef928934e71da1ca67d8c">pcl::FernTrainer::data_set_</a></div><div class="ttdeci">DataSet data_set_</div><div class="ttdoc">The training data set.</div><div class="ttdef"><b>Definition:</b> fern_trainer.h:170</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#a954e9758098d4f7718b649f2866a0ba6">&#9670;&nbsp;</a></span>train()</h2>

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<div class="memproto">
<div class="memtemplate">
template&lt;class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType &gt; </div>
      <table class="memname">
        <tr>
          <td class="memname">void <a class="el" href="classpcl_1_1_fern_trainer.html">pcl::FernTrainer</a>&lt; FeatureType, DataSet, LabelType, ExampleIndex, NodeType &gt;::train </td>
          <td>(</td>
          <td class="paramtype"><a class="el" href="classpcl_1_1_fern.html">pcl::Fern</a>&lt; FeatureType, NodeType &gt; &amp;&#160;</td>
          <td class="paramname"><em>fern</em></td><td>)</td>
          <td></td>
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<p>Trains a decision tree using the set training data and settings. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[out]</td><td class="paramname">fern</td><td>Destination for the trained tree. </td></tr>
  </table>
  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;{</div>
<div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;  <span class="keyword">const</span> <span class="keywordtype">size_t</span> num_of_branches = <a class="code" href="classpcl_1_1_fern_trainer.html#aff950066a956e60191a56ba25a976010">stats_estimator_</a>-&gt;<a class="code" href="classpcl_1_1_stats_estimator.html#aada85e9e1bc3116b661bd4f985843ca0">getNumOfBranches</a> ();</div>
<div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;  <span class="keyword">const</span> <span class="keywordtype">size_t</span> num_of_examples = <a class="code" href="classpcl_1_1_fern_trainer.html#ae1e4719f1772e3b9f7e1fe6e6440c7c0">examples_</a>.size ();</div>
<div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160; </div>
<div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;  <span class="comment">// create random features</span></div>
<div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;  std::vector&lt;FeatureType&gt; features;</div>
<div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;  <a class="code" href="classpcl_1_1_fern_trainer.html#ae2d6f63d91d81db9501befb224a4eded">feature_handler_</a>-&gt;createRandomFeatures (<a class="code" href="classpcl_1_1_fern_trainer.html#aebcda0ed2e8099602c7852abb5787296">num_of_features_</a>, features);</div>
<div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160; </div>
<div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;  <span class="comment">// setup fern</span></div>
<div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;  fern.<a class="code" href="classpcl_1_1_fern.html#a0fd5f5bec392df0e63751d3f5b7da73c">initialize</a> (<a class="code" href="classpcl_1_1_fern_trainer.html#a03a692d065ffc34771ce493ccb0d04af">fern_depth_</a>);</div>
<div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160; </div>
<div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;  <span class="comment">// evaluate all features</span></div>
<div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;  std::vector&lt;std::vector&lt;float&gt; &gt; feature_results (<a class="code" href="classpcl_1_1_fern_trainer.html#aebcda0ed2e8099602c7852abb5787296">num_of_features_</a>);</div>
<div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;  std::vector&lt;std::vector&lt;unsigned char&gt; &gt; flags (<a class="code" href="classpcl_1_1_fern_trainer.html#aebcda0ed2e8099602c7852abb5787296">num_of_features_</a>);</div>
<div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160; </div>
<div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> feature_index = 0; feature_index &lt; <a class="code" href="classpcl_1_1_fern_trainer.html#aebcda0ed2e8099602c7852abb5787296">num_of_features_</a>; ++feature_index)</div>
<div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;  {</div>
<div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;    feature_results[feature_index].reserve (num_of_examples);</div>
<div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;    flags[feature_index].reserve (num_of_examples);</div>
<div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160; </div>
<div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;    <a class="code" href="classpcl_1_1_fern_trainer.html#ae2d6f63d91d81db9501befb224a4eded">feature_handler_</a>-&gt;evaluateFeature (features[feature_index],</div>
<div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;                                       <a class="code" href="classpcl_1_1_fern_trainer.html#a87f77780925ef928934e71da1ca67d8c">data_set_</a>,</div>
<div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;                                       <a class="code" href="classpcl_1_1_fern_trainer.html#ae1e4719f1772e3b9f7e1fe6e6440c7c0">examples_</a>,</div>
<div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;                                       feature_results[feature_index],</div>
<div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;                                       flags[feature_index] );</div>
<div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;  }</div>
<div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160; </div>
<div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;  <span class="comment">// iteratively select features and thresholds</span></div>
<div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;  std::vector&lt;std::vector&lt;std::vector&lt;float&gt; &gt; &gt; branch_feature_results (<a class="code" href="classpcl_1_1_fern_trainer.html#aebcda0ed2e8099602c7852abb5787296">num_of_features_</a>); <span class="comment">// [feature_index][branch_index][result_index]</span></div>
<div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;  std::vector&lt;std::vector&lt;std::vector&lt;unsigned char&gt; &gt; &gt; branch_flags (<a class="code" href="classpcl_1_1_fern_trainer.html#aebcda0ed2e8099602c7852abb5787296">num_of_features_</a>); <span class="comment">// [feature_index][branch_index][flag_index]</span></div>
<div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;  std::vector&lt;std::vector&lt;std::vector&lt;ExampleIndex&gt; &gt; &gt; branch_examples (<a class="code" href="classpcl_1_1_fern_trainer.html#aebcda0ed2e8099602c7852abb5787296">num_of_features_</a>); <span class="comment">// [feature_index][branch_index][result_index]</span></div>
<div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;  std::vector&lt;std::vector&lt;std::vector&lt;LabelType&gt; &gt; &gt; branch_label_data (<a class="code" href="classpcl_1_1_fern_trainer.html#aebcda0ed2e8099602c7852abb5787296">num_of_features_</a>); <span class="comment">// [feature_index][branch_index][flag_index]</span></div>
<div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;  </div>
<div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;  <span class="comment">// - initialize branch feature results and flags</span></div>
<div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> feature_index = 0; feature_index &lt; <a class="code" href="classpcl_1_1_fern_trainer.html#aebcda0ed2e8099602c7852abb5787296">num_of_features_</a>; ++feature_index)</div>
<div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;  {</div>
<div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;    branch_feature_results[feature_index].resize (1);</div>
<div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;    branch_flags[feature_index].resize (1);</div>
<div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;    branch_examples[feature_index].resize (1);</div>
<div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;    branch_label_data[feature_index].resize (1);</div>
<div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160; </div>
<div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;    branch_feature_results[feature_index][0] = feature_results[feature_index];</div>
<div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;    branch_flags[feature_index][0] = flags[feature_index];</div>
<div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;    branch_examples[feature_index][0] = <a class="code" href="classpcl_1_1_fern_trainer.html#ae1e4719f1772e3b9f7e1fe6e6440c7c0">examples_</a>;</div>
<div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;    branch_label_data[feature_index][0] = <a class="code" href="classpcl_1_1_fern_trainer.html#a90bbfae4e2ceb1a5469c6933b976964c">label_data_</a>;</div>
<div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;  }</div>
<div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160; </div>
<div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> depth_index = 0; depth_index &lt; <a class="code" href="classpcl_1_1_fern_trainer.html#a03a692d065ffc34771ce493ccb0d04af">fern_depth_</a>; ++depth_index)</div>
<div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;  {</div>
<div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;    <span class="comment">// get thresholds</span></div>
<div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;    std::vector&lt;std::vector&lt;float&gt; &gt; thresholds (<a class="code" href="classpcl_1_1_fern_trainer.html#aebcda0ed2e8099602c7852abb5787296">num_of_features_</a>);</div>
<div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160; </div>
<div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> feature_index = 0; feature_index &lt; <a class="code" href="classpcl_1_1_fern_trainer.html#aebcda0ed2e8099602c7852abb5787296">num_of_features_</a>; ++feature_index)</div>
<div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;    {</div>
<div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;      thresholds.reserve (<a class="code" href="classpcl_1_1_fern_trainer.html#ac367be1da0dc3cad1c8540b41b20cf20">num_of_thresholds_</a>);</div>
<div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;      <a class="code" href="classpcl_1_1_fern_trainer.html#a512ae37a9a02fed93fb47841ef3b0fad">createThresholdsUniform</a> (<a class="code" href="classpcl_1_1_fern_trainer.html#ac367be1da0dc3cad1c8540b41b20cf20">num_of_thresholds_</a>, feature_results[feature_index], thresholds[feature_index]);</div>
<div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;    }</div>
<div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160; </div>
<div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;    <span class="comment">// compute information gain</span></div>
<div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;    <span class="keywordtype">int</span> best_feature_index = -1;</div>
<div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;    <span class="keywordtype">float</span> best_feature_threshold = 0.0f;</div>
<div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;    <span class="keywordtype">float</span> best_feature_information_gain = 0.0f;</div>
<div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160; </div>
<div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> feature_index = 0; feature_index &lt; <a class="code" href="classpcl_1_1_fern_trainer.html#aebcda0ed2e8099602c7852abb5787296">num_of_features_</a>; ++feature_index)</div>
<div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;    {</div>
<div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> threshold_index = 0; threshold_index &lt; <a class="code" href="classpcl_1_1_fern_trainer.html#ac367be1da0dc3cad1c8540b41b20cf20">num_of_thresholds_</a>; ++threshold_index)</div>
<div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;      {</div>
<div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;        <span class="keywordtype">float</span> information_gain = 0.0f;</div>
<div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> branch_index = 0; branch_index &lt; branch_feature_results[feature_index].size (); ++branch_index)</div>
<div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;        {</div>
<div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;          <span class="keyword">const</span> <span class="keywordtype">float</span> branch_information_gain = <a class="code" href="classpcl_1_1_fern_trainer.html#aff950066a956e60191a56ba25a976010">stats_estimator_</a>-&gt;<a class="code" href="classpcl_1_1_stats_estimator.html#a4794b4417d32e2844bb137ce7934f905">computeInformationGain</a> (<a class="code" href="classpcl_1_1_fern_trainer.html#a87f77780925ef928934e71da1ca67d8c">data_set_</a>,</div>
<div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;                                                                                          branch_examples[feature_index][branch_index],</div>
<div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;                                                                                          branch_label_data[feature_index][branch_index],</div>
<div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;                                                                                          branch_feature_results[feature_index][branch_index],</div>
<div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;                                                                                          branch_flags[feature_index][branch_index],</div>
<div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;                                                                                          thresholds[feature_index][threshold_index]);</div>
<div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160; </div>
<div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;          information_gain += branch_information_gain * branch_feature_results[feature_index][branch_index].size ();</div>
<div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;        }</div>
<div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160; </div>
<div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;        <span class="keywordflow">if</span> (information_gain &gt; best_feature_information_gain)</div>
<div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;        {</div>
<div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;          best_feature_information_gain = information_gain;</div>
<div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;          best_feature_index = <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (feature_index);</div>
<div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;          best_feature_threshold = thresholds[feature_index][threshold_index];</div>
<div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;        }</div>
<div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;      }</div>
<div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;    }</div>
<div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160; </div>
<div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;    <span class="comment">// add feature to the feature list of the fern</span></div>
<div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;    fern.<a class="code" href="classpcl_1_1_fern.html#ae7fd7ac6d00ce5c5802ee045e3563d4a">accessFeature</a> (depth_index) = features[best_feature_index];</div>
<div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;    fern.<a class="code" href="classpcl_1_1_fern.html#ad8adce44d84b61469c9d74622ace1ace">accessThreshold</a> (depth_index) = best_feature_threshold;</div>
<div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160; </div>
<div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;    <span class="comment">// update branch feature results and flags</span></div>
<div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> feature_index = 0; feature_index &lt; <a class="code" href="classpcl_1_1_fern_trainer.html#aebcda0ed2e8099602c7852abb5787296">num_of_features_</a>; ++feature_index)</div>
<div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;    {</div>
<div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;      std::vector&lt;std::vector&lt;float&gt; &gt; &amp; cur_branch_feature_results = branch_feature_results[feature_index];</div>
<div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;      std::vector&lt;std::vector&lt;unsigned char&gt; &gt; &amp; cur_branch_flags = branch_flags[feature_index];</div>
<div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;      std::vector&lt;std::vector&lt;ExampleIndex&gt; &gt; &amp; cur_branch_examples = branch_examples[feature_index];</div>
<div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;      std::vector&lt;std::vector&lt;LabelType&gt; &gt; &amp; cur_branch_label_data = branch_label_data[feature_index];</div>
<div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160; </div>
<div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;      <span class="keyword">const</span> <span class="keywordtype">size_t</span> total_num_of_new_branches = num_of_branches * cur_branch_feature_results.size ();</div>
<div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160; </div>
<div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;      std::vector&lt;std::vector&lt;float&gt; &gt; new_branch_feature_results (total_num_of_new_branches); <span class="comment">// [branch_index][example_index]</span></div>
<div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;      std::vector&lt;std::vector&lt;unsigned char&gt; &gt; new_branch_flags (total_num_of_new_branches); <span class="comment">// [branch_index][example_index]</span></div>
<div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;      std::vector&lt;std::vector&lt;ExampleIndex&gt; &gt; new_branch_examples (total_num_of_new_branches); <span class="comment">// [branch_index][example_index]</span></div>
<div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;      std::vector&lt;std::vector&lt;LabelType&gt; &gt; new_branch_label_data (total_num_of_new_branches); <span class="comment">// [branch_index][example_index]</span></div>
<div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160; </div>
<div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> branch_index = 0; branch_index &lt; cur_branch_feature_results.size (); ++branch_index)</div>
<div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;      {</div>
<div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">size_t</span> num_of_examples_in_this_branch = cur_branch_feature_results[branch_index].size ();</div>
<div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160; </div>
<div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;        std::vector&lt;unsigned char&gt; branch_indices;</div>
<div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;        branch_indices.reserve (num_of_examples_in_this_branch);</div>
<div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160; </div>
<div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;        <a class="code" href="classpcl_1_1_fern_trainer.html#aff950066a956e60191a56ba25a976010">stats_estimator_</a>-&gt;<a class="code" href="classpcl_1_1_stats_estimator.html#aa3fda8a830fbaded719ac28b2d6667bb">computeBranchIndices</a> (cur_branch_feature_results[branch_index],</div>
<div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;                                                cur_branch_flags[branch_index],</div>
<div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;                                                best_feature_threshold,</div>
<div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;                                                branch_indices);</div>
<div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160; </div>
<div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160;        <span class="comment">// split results into different branches</span></div>
<div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">size_t</span> base_branch_index = branch_index * num_of_branches;</div>
<div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> example_index = 0; example_index &lt; num_of_examples_in_this_branch; ++example_index)</div>
<div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;        {</div>
<div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;          <span class="keyword">const</span> <span class="keywordtype">size_t</span> combined_branch_index = base_branch_index + branch_indices[example_index];</div>
<div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160; </div>
<div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;          new_branch_feature_results[combined_branch_index].push_back (cur_branch_feature_results[branch_index][example_index]);</div>
<div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;          new_branch_flags[combined_branch_index].push_back (cur_branch_flags[branch_index][example_index]);</div>
<div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;          new_branch_examples[combined_branch_index].push_back (cur_branch_examples[branch_index][example_index]);</div>
<div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;          new_branch_label_data[combined_branch_index].push_back (cur_branch_label_data[branch_index][example_index]);</div>
<div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;        }</div>
<div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;      }</div>
<div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160; </div>
<div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;      branch_feature_results[feature_index] = new_branch_feature_results;</div>
<div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160;      branch_flags[feature_index] = new_branch_flags;</div>
<div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;      branch_examples[feature_index] = new_branch_examples;</div>
<div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;      branch_label_data[feature_index] = new_branch_label_data;</div>
<div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;    }</div>
<div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;  }</div>
<div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160; </div>
<div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;  <span class="comment">// set node statistics</span></div>
<div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;  <span class="comment">// - re-evaluate selected features</span></div>
<div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;  std::vector&lt;std::vector&lt;float&gt; &gt; final_feature_results (<a class="code" href="classpcl_1_1_fern_trainer.html#a03a692d065ffc34771ce493ccb0d04af">fern_depth_</a>); <span class="comment">// [feature_index][example_index]</span></div>
<div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;  std::vector&lt;std::vector&lt;unsigned char&gt; &gt; final_flags (<a class="code" href="classpcl_1_1_fern_trainer.html#a03a692d065ffc34771ce493ccb0d04af">fern_depth_</a>); <span class="comment">// [feature_index][example_index]</span></div>
<div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;  std::vector&lt;std::vector&lt;unsigned char&gt; &gt; final_branch_indices (<a class="code" href="classpcl_1_1_fern_trainer.html#a03a692d065ffc34771ce493ccb0d04af">fern_depth_</a>); <span class="comment">// [feature_index][example_index]</span></div>
<div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> depth_index = 0; depth_index &lt; <a class="code" href="classpcl_1_1_fern_trainer.html#a03a692d065ffc34771ce493ccb0d04af">fern_depth_</a>; ++depth_index)</div>
<div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;  {</div>
<div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;    final_feature_results[depth_index].reserve (num_of_examples);</div>
<div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;    final_flags[depth_index].reserve (num_of_examples);</div>
<div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;    final_branch_indices[depth_index].reserve (num_of_examples);</div>
<div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160; </div>
<div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;    <a class="code" href="classpcl_1_1_fern_trainer.html#ae2d6f63d91d81db9501befb224a4eded">feature_handler_</a>-&gt;evaluateFeature (fern.<a class="code" href="classpcl_1_1_fern.html#ae7fd7ac6d00ce5c5802ee045e3563d4a">accessFeature</a> (depth_index),</div>
<div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;                                       <a class="code" href="classpcl_1_1_fern_trainer.html#a87f77780925ef928934e71da1ca67d8c">data_set_</a>,</div>
<div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160;                                       <a class="code" href="classpcl_1_1_fern_trainer.html#ae1e4719f1772e3b9f7e1fe6e6440c7c0">examples_</a>,</div>
<div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;                                       final_feature_results[depth_index],</div>
<div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;                                       final_flags[depth_index] );</div>
<div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160; </div>
<div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;    <a class="code" href="classpcl_1_1_fern_trainer.html#aff950066a956e60191a56ba25a976010">stats_estimator_</a>-&gt;<a class="code" href="classpcl_1_1_stats_estimator.html#aa3fda8a830fbaded719ac28b2d6667bb">computeBranchIndices</a> (final_feature_results[depth_index],</div>
<div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;                                            final_flags[depth_index],</div>
<div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;                                            fern.<a class="code" href="classpcl_1_1_fern.html#ad8adce44d84b61469c9d74622ace1ace">accessThreshold</a> (depth_index),</div>
<div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;                                            final_branch_indices[depth_index]);</div>
<div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;  }</div>
<div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160; </div>
<div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;  <span class="comment">// - distribute examples to nodes</span></div>
<div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;  std::vector&lt;std::vector&lt;LabelType&gt; &gt; node_labels (0x1 &lt;&lt; <a class="code" href="classpcl_1_1_fern_trainer.html#a03a692d065ffc34771ce493ccb0d04af">fern_depth_</a>); <span class="comment">// [node_index][example_index]</span></div>
<div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;  std::vector&lt;std::vector&lt;ExampleIndex&gt; &gt; node_examples (0x1 &lt;&lt; <a class="code" href="classpcl_1_1_fern_trainer.html#a03a692d065ffc34771ce493ccb0d04af">fern_depth_</a>); <span class="comment">// [node_index][example_index]</span></div>
<div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160; </div>
<div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> example_index = 0; example_index &lt; num_of_examples; ++example_index)</div>
<div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;  {</div>
<div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;    <span class="keywordtype">size_t</span> node_index = 0;</div>
<div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> depth_index = 0; depth_index &lt; <a class="code" href="classpcl_1_1_fern_trainer.html#a03a692d065ffc34771ce493ccb0d04af">fern_depth_</a>; ++depth_index)</div>
<div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;    {</div>
<div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;      node_index *= num_of_branches;</div>
<div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;      node_index += final_branch_indices[depth_index][example_index];</div>
<div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;    }</div>
<div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160; </div>
<div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;    node_labels[node_index].push_back (<a class="code" href="classpcl_1_1_fern_trainer.html#a90bbfae4e2ceb1a5469c6933b976964c">label_data_</a>[example_index]);</div>
<div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;    node_examples[node_index].push_back (<a class="code" href="classpcl_1_1_fern_trainer.html#ae1e4719f1772e3b9f7e1fe6e6440c7c0">examples_</a>[example_index]);</div>
<div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;  }</div>
<div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160; </div>
<div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;  <span class="comment">// - compute and set statistics for every node</span></div>
<div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;  <span class="keyword">const</span> <span class="keywordtype">size_t</span> num_of_nodes = 0x1 &lt;&lt; <a class="code" href="classpcl_1_1_fern_trainer.html#a03a692d065ffc34771ce493ccb0d04af">fern_depth_</a>;</div>
<div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> node_index = 0; node_index &lt; num_of_nodes; ++node_index)</div>
<div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;  {</div>
<div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;    <a class="code" href="classpcl_1_1_fern_trainer.html#aff950066a956e60191a56ba25a976010">stats_estimator_</a>-&gt;<a class="code" href="classpcl_1_1_stats_estimator.html#a6caa1bf87f7cb0b697d4fc081f0339af">computeAndSetNodeStats</a> (<a class="code" href="classpcl_1_1_fern_trainer.html#a87f77780925ef928934e71da1ca67d8c">data_set_</a>, node_examples[node_index], node_labels[node_index], fern[node_index]);</div>
<div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;  }</div>
<div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;}</div>
<div class="ttc" id="aclasspcl_1_1_fern_html_a0fd5f5bec392df0e63751d3f5b7da73c"><div class="ttname"><a href="classpcl_1_1_fern.html#a0fd5f5bec392df0e63751d3f5b7da73c">pcl::Fern::initialize</a></div><div class="ttdeci">void initialize(const size_t num_of_decisions)</div><div class="ttdoc">Initializes the fern.</div><div class="ttdef"><b>Definition:</b> fern.h:71</div></div>
<div class="ttc" id="aclasspcl_1_1_fern_html_ad8adce44d84b61469c9d74622ace1ace"><div class="ttname"><a href="classpcl_1_1_fern.html#ad8adce44d84b61469c9d74622ace1ace">pcl::Fern::accessThreshold</a></div><div class="ttdeci">float &amp; accessThreshold(const size_t threshold_index)</div><div class="ttdoc">Access operator for thresholds.</div><div class="ttdef"><b>Definition:</b> fern.h:186</div></div>
<div class="ttc" id="aclasspcl_1_1_fern_html_ae7fd7ac6d00ce5c5802ee045e3563d4a"><div class="ttname"><a href="classpcl_1_1_fern.html#ae7fd7ac6d00ce5c5802ee045e3563d4a">pcl::Fern::accessFeature</a></div><div class="ttdeci">FeatureType &amp; accessFeature(const size_t feature_index)</div><div class="ttdoc">Access operator for features.</div><div class="ttdef"><b>Definition:</b> fern.h:168</div></div>
<div class="ttc" id="aclasspcl_1_1_fern_trainer_html_a512ae37a9a02fed93fb47841ef3b0fad"><div class="ttname"><a href="classpcl_1_1_fern_trainer.html#a512ae37a9a02fed93fb47841ef3b0fad">pcl::FernTrainer::createThresholdsUniform</a></div><div class="ttdeci">static void createThresholdsUniform(const size_t num_of_thresholds, std::vector&lt; float &gt; &amp;values, std::vector&lt; float &gt; &amp;thresholds)</div><div class="ttdoc">Creates uniformely distrebuted thresholds over the range of the supplied values.</div><div class="ttdef"><b>Definition:</b> fern_trainer.hpp:259</div></div>
<div class="ttc" id="aclasspcl_1_1_stats_estimator_html_a4794b4417d32e2844bb137ce7934f905"><div class="ttname"><a href="classpcl_1_1_stats_estimator.html#a4794b4417d32e2844bb137ce7934f905">pcl::StatsEstimator::computeInformationGain</a></div><div class="ttdeci">virtual float computeInformationGain(DataSet &amp;data_set, std::vector&lt; ExampleIndex &gt; &amp;examples, std::vector&lt; LabelDataType &gt; &amp;label_data, std::vector&lt; float &gt; &amp;results, std::vector&lt; unsigned char &gt; &amp;flags, const float threshold) const =0</div><div class="ttdoc">Computes the information gain obtained by the specified threshold on the supplied feature evaluation ...</div></div>
<div class="ttc" id="aclasspcl_1_1_stats_estimator_html_a6caa1bf87f7cb0b697d4fc081f0339af"><div class="ttname"><a href="classpcl_1_1_stats_estimator.html#a6caa1bf87f7cb0b697d4fc081f0339af">pcl::StatsEstimator::computeAndSetNodeStats</a></div><div class="ttdeci">virtual void computeAndSetNodeStats(DataSet &amp;data_set, std::vector&lt; ExampleIndex &gt; &amp;examples, std::vector&lt; LabelDataType &gt; &amp;label_data, NodeType &amp;node) const =0</div><div class="ttdoc">Computes and sets the statistics for a node.</div></div>
<div class="ttc" id="aclasspcl_1_1_stats_estimator_html_aa3fda8a830fbaded719ac28b2d6667bb"><div class="ttname"><a href="classpcl_1_1_stats_estimator.html#aa3fda8a830fbaded719ac28b2d6667bb">pcl::StatsEstimator::computeBranchIndices</a></div><div class="ttdeci">virtual void computeBranchIndices(std::vector&lt; float &gt; &amp;results, std::vector&lt; unsigned char &gt; &amp;flags, const float threshold, std::vector&lt; unsigned char &gt; &amp;branch_indices) const =0</div><div class="ttdoc">Computes the branch indices obtained by the specified threshold on the supplied feature evaluation re...</div></div>
<div class="ttc" id="aclasspcl_1_1_stats_estimator_html_aada85e9e1bc3116b661bd4f985843ca0"><div class="ttname"><a href="classpcl_1_1_stats_estimator.html#aada85e9e1bc3116b661bd4f985843ca0">pcl::StatsEstimator::getNumOfBranches</a></div><div class="ttdeci">virtual size_t getNumOfBranches() const =0</div><div class="ttdoc">Returns the number of brances a node can have (e.g. a binary tree has 2).</div></div>
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